Bayesian Measurement Masks for GNSS Positioning

Marcus Greiff, Stefano Di Cairano, Karl Berntorp

Abstract: We propose a Bayesian measurement masking method for global navigation satellite system (GNSS) positioning to mitigate disturbances from multi-path biases and modeling errors. The method removes erroneous GNSS observations to improve performance in downstream positioning algorithms. The measurement masking is posed as a binary classification problem, and solved by sequentially determining the noise statistics of individual pseudo-range measurements in the GNSS observations. Bayesian probabilities of mismatching noise models inform when outlier events such as multipath or non-line-of-sight (NLOS) events occur. We report a classification F1-score of $\gt0.99$ when the modeling assumptions are satisfied, and $\gt0.97$ when realistic modeling errors are included, both for dynamic and static receiver motion models.